3 reasons why MySQL HeatWave on AWS is better
than Amazon Aurora and Redshift or Snowflake
Here are the top three reasons to choose Oracle MySQL HeatWave on Amazon Web Services (AWS) over Amazon Aurora and Amazon Redshift with AQUA and over Amazon Aurora and Snowflake.
- Simplicity: Transactions, real-time analytics, and machine learning (ML) in one service, without ETL.
- Better price performance: Up to 10X better throughput than Amazon Aurora, 7X better price performance than Amazon Redshift with AQUA, and 10X better price performance than Snowflake on AWS. MySQL Autopilot further improves performance.
- Increased data protection: Advanced security features protect data throughout its lifecycle, supporting compliance with regulatory requirements.
“MySQL HeatWave on AWS simplifies our data platform with a consolidated database for both transaction processing and analytics. We have seen 60-90X faster complex queries compared to AWS RDS and Aurora that generate the real-time analytics we need for targeted, multichannel campaigns.”
1. Simplicity
Capability and evidence |
MySQL HeatWave on AWS |
Amazon Aurora and Redshift |
Amazon Aurora and Snowflake |
|---|---|---|---|
One database service for OLTP and OLAP workloads on AWS |
yes
Customers can run OLTP and OLAP workloads in a single database service—without changes to current applications based on MySQL and Aurora. |
no
Amazon Aurora is for OLTP; customers need a separate OLAP service, such as Redshift. |
no
Amazon Aurora is for OLTP; customers need a separate OLAP service, such as Snowflake. Snowflake’s Unistore is only in preview. |
No ETL duplication |
yes
The complex, time-consuming, and expensive ETL is eliminated. |
no
Single-purpose databases require an ETL process to move data between OLTP and OLAP services. |
no
Single-purpose databases require an ETL process to move data between OLTP and OLAP services. |
Real-time, secure analytics |
yes
Queries always access the most up-to-date data; there’s no data transfer between databases. |
no
By the time data goes through ETL and is available in Redshift, it’s already stale. Moving data between stores can present additional security risks. |
no
By the time data goes through ETL and is available in Snowflake, it’s already stale. Moving data between stores can present additional security risks. |
In-database machine learning |
yes
With HeatWave AutoML, developers and data analysts can build, train, deploy, and explain machine learning models within MySQL HeatWave. |
no
A separate ML service, such as Amazon SageMaker, is required. |
no
A separate ML service, such as Amazon SageMaker, is required. |
Explainable data models |
yes
All models are explainable, increasing trust, fairness, causality, and repeatability and helping with regulatory compliance. |
no
All models in Aurora ML and Redshift ML aren’t explainable, which may reduce trust, increase risks for bias, and could make regulatory compliance more difficult. |
no
All models in Aurora ML aren’t explainable, which may reduce trust, increase risks for bias, and could make regulatory compliance more difficult. Snowflake requires a third-party ML service. |
Automated machine learning lifecycle |
yes
The ML lifecycle is fully automated, including algorithm selection, intelligent data sampling, feature selection, and hyper-parameter tuning. |
no
Aurora ML and Redshift ML require data science expertise to influence the performance, accuracy, and cost of training. |
no
Aurora ML requires data science expertise to influence the performance, accuracy, and cost of training. Snowflake requires a third-party ML service. |
Interactive ML console |
yes
An interactive console lets business analysts build, train, run, and explain ML models using a visual interface—without using SQL commands or any coding. |
no
Neither Aurora ML nor Redshift ML provide an interactive console for business analysts to manage ML models. Users are expected to build ML models using SQL. |
no
Neither Aurora ML nor Snowflake provide an interactive console for business analysts to manage ML models. Aurora users are expected to build ML models using SQL. Snowflake supports importing ML models created using third-party services. |
2. Better price performance
MySQL HeatWave on AWS delivers up to 10X better throughput than Amazon Aurora with MySQL Autopilot, as demonstrated by a TPC-C benchmark.
MySQL HeatWave on AWS delivers 7X better price performance than Amazon Redshift with AQUA, as demonstrated by a TPC-H benchmark.
MySQL HeatWave on AWS delivers 10X better price performance than Snowflake on AWS, as demonstrated by a TPC-H benchmark.
MySQL Autopilot automates many of the most important and often challenging aspects of achieving high query performance at scale.
Capability and evidence |
MySQL HeatWave on AWS |
Amazon Aurora and Redshift |
Amazon Aurora and Snowflake |
|---|---|---|---|
Machine learning–powered automation |
yes
MySQL Autopilot automates provisioning, data loading, query execution, and failure handling—further improving performance while saving developers and DBAs significant time. |
no
Built-in machine learning–powered automation isn’t available. Expertise in both databases and manual operations is required. |
no
Built-in machine learning–powered automation isn’t available. Expertise in both databases and manual operations is required. |
Automated workload-aware tuning for OLTP |
yes
MySQL Autopilot delivers high OLTP throughput that's sustained at high levels of transactions and concurrency. |
no
With Aurora the throughput of the system drops at high levels of transactions and concurrency. Redshift can’t be used for OLTP. |
no
With Aurora the throughput of the system drops at high levels of transactions and concurrency. Snowflake can’t be used for OLTP. |
Automated query performance tuning |
yes
MySQL Autopilot learns from the execution of queries to automatically improve the performance of subsequent queries. |
no
Query plans aren’t automatically improved based on previously executed queries. |
no
Query plans aren’t automatically improved based on previously executed queries. |
Automated scheduling of query execution |
yes
MySQL Autopilot prioritizes short-running queries without penalizing long-running queries. |
no
Queries are executed in a first in, first out order, which penalizes the performance of short-running queries. |
no
Queries are executed in a first in, first out order, which penalizes the performance of short-running queries. |
Automated provisioning of the optimal cluster size |
yes
MySQL Autopilot autoprovisions the optimal cluster size for a given dataset. |
no
Developers and DBAs must guess or manually estimate by trial and error the optimal size of the cluster for both databases. |
no
Developers and DBAs must guess or manually estimate by trial and error the optimal size of the cluster for both databases. |
3. Increased data protection
Capability and evidence |
MySQL HeatWave on AWS |
Amazon Aurora and Redshift |
Amazon Aurora and Snowflake |
|---|---|---|---|
Digital signatures to confirm the authenticity and integrity of messages |
yes
Built-in server-side asymmetric encryption with key generation and digital signatures. |
no
Built-in server-side asymmetric encryption to implement digital signatures isn’t provided. |
no
Built-in server-side asymmetric encryption to implement digital signatures isn’t provided. |
Built-in server-side data masking |
yes
Data masking and deidentification are built in, helping to protect the confidentiality of private data. |
no
Data masking and deidentification need to be implemented at the application level. |
no
For Aurora data masking and deidentification need to be implemented at the application level. |
Built-in server-side database firewall |
yes
Helps protect against various types of attacks, including some database-specific threats such as SQL injection. |
no
A built-in server-side database firewall isn’t provided, leaving the database vulnerable to ransomware attacks. |
no
A built-in server-side database firewall isn’t provided, leaving the database vulnerable to ransomware attacks. |
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